{"type":"ScholarlyArticle","title":"Detecting Adversarial Faces Using Only Real Face Self-Perturbations","authors":[{"name":"Qian Wang","slug":"qian-wang","given":"Qian","family":"Wang","affiliation":"Huazhong University of Science and Technology , Wuhan , China; {yqwq1996 ,"},{"name":"Yongqin Xian","slug":"yongqin-xian","given":"Yongqin","family":"Xian","affiliation":"Google , Switzerland"},{"name":"Hefei Ling","slug":"hefei-ling","given":"Hefei","family":"Ling","affiliation":"Huazhong University of Science and Technology , Wuhan , China; {yqwq1996 ,"},{"name":"Jinyuan Zhang","slug":"jinyuan-zhang","given":"Jinyuan","family":"Zhang","affiliation":"Software Development Center , Industrial and Commercial Bank of China"},{"name":"Xiaorui Lin","slug":"xiaorui-lin","given":"Xiaorui","family":"Lin","affiliation":"Software Development Center , Industrial and Commercial Bank of China"},{"name":"Ping Li","slug":"ping-li","given":"Ping","family":"Li","affiliation":"Huazhong University of Science and Technology , Wuhan , China; {yqwq1996 ,"},{"name":"Jiazhong Chen","slug":"jiazhong-chen","given":"Jiazhong","family":"Chen","affiliation":"Huazhong University of Science and Technology , Wuhan , China; {yqwq1996 ,"},{"name":"Ning Yu","slug":"ning-yu","given":"Ning","family":"Yu","affiliation":"Salesforce Research , USA"}],"abstract":"Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when applied to facial recognition systems. Although existing defense techniques achieve high accuracy in detecting some specific adversarial faces (adv-faces), new attack methods especially GAN-based attacks with completely different noise patterns circumvent them and reach a higher attack success rate. Even worse, existing techniques require attack data before implementing the defense, making it impractical to defend newly emerging attacks that are unseen to defenders. In this paper, we investigate the intrinsic generality of adv-faces and propose to generate pseudo adv-faces by perturbing real faces with three heuristically designed noise patterns. We are the first to train an adv-face detector using only real faces and their self-perturbations, agnostic to victim facial recognition systems, and agnostic to unseen attacks. By regarding adv-faces as out-of-distribution data, we then naturally introduce a novel cascaded system for adv-face detection, which consists of training data self-perturbations, decision boundary regularization, and a max-pooling-based binary classifier focusing on abnormal local color aberrations. Experiments conducted on LFW and CelebA-HQ datasets with eight gradient-based and two GAN-based attacks validate that our method generalizes to a variety of unseen adversarial attacks.","doi":"10.24963/ijcai.2023/165","url":"https://ijcai.org/proceedings/2023/165","pdf":"https://ijcai.org/proceedings/2023/0165.pdf","markdown":"https://ijcai.org/proceedings/2023/165.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/165","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":165,"pages":{"first":1488,"last":1496},"keywords":["Computer Vision: CV: Biometrics, face, gesture and pose recognition","Computer Vision: CV: Adversarial learning, adversarial attack and defense methods","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Avarikioti et al., 2019] Georgia Avarikioti, Kenan Besic, Yuyi Wang, and Roger Wattenhofer. 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